Machine Learning for Finance

Preface

Introduction

• Introduction
• How machines are taught
• Factors contributing to the success of machine learning
• Machine learning and artificial intelligence
• Machine learning and deep learning
• Machine learning and statistics
• Machine learning and data mining
• Machine learning in finance
• Importance of machine learning in finance
• Robo-warning
• How to utilize machine learning in finance
• Utilize outsider machine learning arrangements
• Development and combination
• How is machine learning used today
• Conclusion

Naive Bayes, Normal Distribution, and Automatic Clustering

• Introduction
• Naive Bayes
• Normal distribution
• Automatic cluster detection in data mining
• Application of machine learning in cybersecurity
• Conclusion

Machine Learning for Data Structuring

• Introduction
• Data structuring
• The future of big data
• Structured and unstructured data
• Conclusion

Parsing Data Using NLP

• Introduction
• Uses of NLP
• Key advantages of NLP
• Data handling in NLP
• NLP applications
• Conclusion

Computer Vision

• Introduction
• Computer vision application
• Neural networks in computer vision
• Overview of computer vision
• Image recognition
• Biometric recognition
• Software vulnerabilities
• Conclusion

Neural Network, GBM, and Gradient Descent

• Introduction
• Working of neural networks
• Types of neural networks in AI
• Benefits of using artificial neural networks
• Gradient boosting algorithms
• Conclusion

Sequence Modeling

• Introduction
• Word embedding
• Feed-forward neural network algorithm
• Convolutional neural network algorithm
• Recurrent neural networks (RNN) algorithm
• Conditional random field (CRF) algorithm
• Modeling procedure
• Conclusion

Reinforcement Learning for Financial Markets

• Introduction
• Problem types in machine learning
• Identifying key predictors (data reduction)
• Learning from experience (reinforcement learning)
• Reinforcement learning algorithms
• Types of reinforcement learning
• Applications of reinforcement learning in real life
• Conclusion

Finance Use Cases

• Introduction
• Technology and finance
• Automation
• The impact of FinTech
• Guidelines to live by
• Innovative technologies
• Digital bank
• AI as a strategy at the top level
• Development status of different AI technologies
• Risk management
• Fraud detection and prevention
• Improving the truth of financial rules and designs
• Trading
• AI in banking
• Conclusion

Impact of Machine Learning on FinTech

• Introduction
• Overview of FinTech companies
• Impact of technology
• Challenges
• Conclusion

Machine Learning in Finance

• Introduction
• Machine learning use cases in banking
• Security
• Guaranteeing and credit scoring
• Algorithmic exchanging
• Robo-advisors
• Utilize outsider machine learning arrangements
• Applications of machine learning
• Current financial applications
• Machine learning and cryptocurrencies

eKYC and Anti-Fraud Policy

• Introduction
• Big data analytics: True Buzzword of today
• How criminals obtain information for online banking
• Common ways in which information can be stolen
• ATMs
• Security measures
• Conclusion

Uses of Data Mining and Data Visualization

• Introduction
• Data visualization
• Data mining
• Future health care
• Education
• Customer relationship management
• Criminal investigation
• Fraud detection
• Customer segmentation
• Intrusion detection
• Lie detection
• Conclusion

Advantages and Disadvantages of Machine Learning

• Introduction
• Advantages
• Disadvantages
• Conclusion

Applications of Machine Learning in Other Industries

• Introduction
• General applications of machine learning
• Conclusion

Ethical Considerations in Artificial Intelligence

• Introduction
• Loss of jobs
• Inequality
• Humanity
• Disinformation
• Artificial intelligence and crime
• Racist robots
• Artificial intelligence vs. humans
• Conclusion

Artificial Intelligence in Banking

• Introduction
• Fraud detection
• Cost cutting
• Customer service
• Risk management
• Internet banking
• Conclusion

Common Machine Learning Algorithms

• Introduction
• Regression
• k-means clustering
• KNN algorithm
• Principal component analysis (PCA) algorithm
• Polynomial fitting and least squares algorithm
• Forced linear regression algorithm
• Support vector machine (SVM) algorithm
• Conditional random fields (CRFs) algorithm
• Decision tree algorithm
• Conclusion

Frequently Asked Questions

• Conclusion
• Approaching a machine learning problem
• Humans in the loop
• Testing production systems
• Next step
• Machine learning packages
• Where do we go from here?